Towards Scalable Distributed Framework for Urban Congestion Traffic Patterns Warehousing. (6th January 2015)

• Record Type: Journal Article
• Authors: Boulmakoul, A.; Karim, L.; Mandar, M.; Idri, A.; Daissaoui, A.
• Other Names: Yang Yongqing Academic Editor.
• Is Part Of: Applied computational intelligence and soft computing. Volume 2015(2015)
• Publication Date: 2015-01-06
• Subjects: Computational intelligence -- Periodicals; Soft computing -- Periodicals; 006.305
• DOI: 10.1155/2015/578601
• Languages: English
• ISSNs: 1687-9724

Abstract

Abstract : We put forward architecture of a framework for integration of data from moving objects related to urban transportation network. Most of this research refers to the GPS outdoor geolocation technology and uses distributed cloud infrastructure with big data NoSQL database. A network of intelligent mobile sensors, distributed on urban network, produces congestion traffic patterns. Congestion predictions are based on extended simulation model. This model provides traffic indicators calculations, which fuse with the GPS data for allowing estimation of traffic states across the whole network. The discovery process of congestion patterns uses semantic trajectories metamodel given in our previous works. The challenge of the proposed solution is to store patterns of traffic, which aims to ensure the surveillance and intelligent real-time control network to reduce congestion and avoid its consequences. The fusion of real-time data from GPS-enabled smartphones integrated with those provided by existing traffic systems improves traffic congestion knowledge, as well as generating new information for a soft operational control and providing intelligent added value for transportation systems deployment.